模式识别中的机器学习与数据挖掘

　　This book constitutes the refereed proceedings of the 4th International Conference on Machine Learning and Data Mining in Pattern Recognition， MLDM 2005， held in Leipzig， Germany， in July 2005.The 68 revised full papers presented were carefully reviewed and selected. The papers are organized in topical sections on classification and model estimation， neural methods， subspace methods， basics and applications of clustering， feature grouping， discretization， selection and transformation， applications in medicine， time series and sequential pattern mining， mining images in computer vision， mining images and texture， mining motion from sequence， speech analysis， aspects of data mining， text mining， and as a special track： industrial applications of data mining.

暂缺《模式识别中的机器学习与数据挖掘》作者简介

Classification and Model Estimation
On ECOC as Binary Ensemble Classifiers
Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis
Parameter Inference of Cost-Sensitive Boosting Algorithms
Finite Mixture Models with Negative Components
MML-Based Approach for Finite Dirichlet Mixture Estimation and Selection
Principles of Multi-kernel Data Mining
Neural Methods
Comparative Analysis of Genetic Algorithm, Simulated Annealing and Cutting Angle Method for Artificial Neural Networks
Determining Regularization Parameters for Derivative Free Neural Learning
A Comprehensible SOM-Based Scoring System
Subspace Methods
The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets
SSC: Statistical Subspace Clustering
Understanding Patterns with Different Subspace Classification
Clustering: Basics
Using Clustering to Learn Distance Functions for Supervised Similarity Assessment
Linear Manifold Clustering
Universal Clustering with Regularization in Probabilistic Space
Acquisition of Concept Descriptions by Conceptual Clustering
Applications of Clustering
Clustering Large Dynamic Datasets Using Exemplar Points
Birds of a Feather Surf Together: Using Clustering Methods to Improve Navigation Prediction from Internet Log Files
Alarm Clustering for Intrusion Detection Systems in Computer Networks
Clustering Document Images Using Graph Summaries
Feature Grouping, Diseretization, Selection and Transformation
Feature Selection Method Using Preferences Aggregation
……
Applications in Medicine
Time Series and Sequential Pattern Mining
Mining Images in Computer Vision
Mining Images and Texture
Mining Motion from Sequence
Speech Analysis
Aspects of Data Mining
Text Mining
Special Track:Industrial Applecations of Data Mining
Author Index